In the main body of the paper, we mentioned that dominant learned video compression methods suffer from three inherent problems caused by the motion-based video compression framework, and two problems (i.e., the complexity and error propagation problem) are already present in section 3.1. In this section, we will explain the second problem that the inaccuracy of a predictive frame may result in residual errors and then bring more difficulty to the residual compression in the motion-based video compression method.
An accurate optical flow network (i.e., PWCnet [1]}) was used as the motion estimation module in motion-based video compression framework. The compression process is evaluated on three different video sequences, e.g., Beauty and YachtRide in UVG, and BasketballDrive in HEVC Class B. The qualitative results are visualized in Fig REF , Fig REF  and Fig REF .
